Division of Medical Physics, Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Med Phys. 2017 Dec;44(12):6341-6352. doi: 10.1002/mp.12593. Epub 2017 Oct 23.
Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images.
First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity.
The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation.
We show that a CNN can yield accurate estimations of esophagus location, and that the results of this model can be refined by a random walk step taking pixel intensities and neighborhood relationships into account. One of the main advantages of our network over previous methods is that it performs 3D convolutions, thus fully exploiting the 3D spatial context and performing an efficient volume-wise prediction. The whole segmentation process is fully automatic and yields esophagus delineations in very good agreement with the gold standard, showing that it can compete with previously published methods.
在放疗计划中，精确勾画危及器官对于向肿瘤提供高剂量照射而同时保护健康组织至关重要。近年来，自动化分割方法在各种解剖结构的分割中表现出越来越高的性能。然而，对于食管等具有多种形状且与周围组织对比度差的器官，这一任务仍然具有挑战性。对于人类专家来说，从 CT 图像中分割食管是一个耗时且容易出错的过程。为了解决这些问题，我们提出了一种基于 3D 全卷积神经网络（CNN）的随机游走方法，用于自动从 CT 图像中分割食管。
首先，通过 CNN 生成软概率图。然后，将主动轮廓模型（ACM）拟合到 CNN 软概率图上，以获得食管位置的初步估计。然后，将 CNN 和 ACM 的输出与基于 CT 亨氏单位（HU）值的概率模型结合使用，驱动随机游走。在来自两个不同数据集的 50 个 CT 上进行了训练和评估，使用了临床使用的经过同行评审的食管轮廓。结果根据空间重叠和形状相似性进行评估。
与参考轮廓相比，所提出算法生成的食管轮廓的平均 Dice 系数为 0.76 ± 0.11，平均对称平方距离为 1.36 ± 0.90mm，平均 Hausdorff 距离为 11.68 ± 6.80mm。这表明与参考轮廓具有非常好的一致性，并且与现有方法相比精度有所提高。此外，当考虑到 Synapse 数据集的现有文献中报告的结果时，我们的方法优于所有现有的方法，这表明该方法代表了自动食管分割的最新技术水平。
我们表明，CNN 可以准确估计食管的位置，并且可以通过考虑像素强度和邻域关系的随机游走步骤来细化该模型的结果。与以前的方法相比，我们的网络的主要优势之一是它执行 3D 卷积，从而充分利用 3D 空间上下文并进行高效的体积预测。整个分割过程是全自动的，生成的食管勾画与金标准非常吻合，表明它可以与以前发表的方法相媲美。
